Hyperspectral image classification based on multiple reduced kernel extreme learning machine

Abstract

This paper presents an efficient hyperspectral images classification method based on multiple reduced kernel extreme learning machine (MRKELM). The MRKELM model is developed on the basis of the multiple kernel leaning method and the reduced kernel extreme learning machine method. In the presented MRKELM, the kernel function are not fixed anymore, multiple kernels are adaptively trained as a hybrid kernel and the optimal kernel combination weights are jointly optimized. Finally, two simulation examples, classification of benchmark datasets and classification of hyperspectral images including Indian Pines, University of Pavia, and Salinas respectively, are used testify the performance of the proposed MRKELM method.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant no. 61374154 and the National Basic Research Program of China under Grant no. 2013CB430403.

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Correspondence to Fei Lv.

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Lv, F., Han, M. Hyperspectral image classification based on multiple reduced kernel extreme learning machine. Int. J. Mach. Learn. & Cyber. 10, 3397–3405 (2019). https://doi.org/10.1007/s13042-019-00926-5

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Keywords

  • Classification
  • Hyperspectral image
  • Reduced kernel extreme learning machine
  • Multiple reduced kernel extreme learning machine